Soft thresholding has been widely used as an efficient denoising approach in signal processing [1]}, [2]} and recently has drawn attention from deep learning communities [3]}, [4]}. The core idea behind soft thresholding is to use thresholds to distinguish real signals from noises, while the input signals below the thresholds are zeroed and the others are shrinked. Since determining the task-specific thresholds requires significant expertise, Isogawa et al. [3]} proposed a neural network to learn the global thresholds for all inputs, and the learned values are fixed after training. Zhao et al. [4]} designed a residual shrinkage network to produce dynamic thresholds for different inputs. In particular, the thresholds are designed channel-wise and shared among all pixels, and then soft thresholding shrinkage is leveraged to denoise the data channel by channel. However, since the data collection with respect to each pixel can be treated as an independent process, the optimal thresholds for each pixel must be different from each other. Therefore, the global or channel-wise threshold may not be the best solution for denoising the photon counting data.
